Abstract

Extensive knowledge bases of entailment rules
between predicates are crucial for applied semantic
inference. In this paper we propose an
algorithm that utilizes transitivity constraints
to learn a globally-optimal set of entailment
rules for typed predicates. We model the task
as a graph learning problem and suggest methods
that scale the algorithm to larger graphs.
We apply the algorithm over a large data set
of extracted predicate instances, from which a
resource of typed entailment rules has been recently
released (Schoenmackers et al., 2010).
Our results show that using global transitivity
information substantially improves performance
over this resource and several baselines,
and that our scaling methods allow us
to increase the scope of global learning of
entailment-rule graphs.